SMART: Scalable Mesh-free Aerodynamic Simulations from Raw Geometries using a Transformer-based Surrogate Model
arXiv:2601.18707v2 Announce Type: replace-cross Abstract: Machine learning-based surrogate models have emerged as more efficient alternatives to numerical solvers for physical simulations over complex geometries, such as car bodies. Many existing models incorporate the simulation mesh as an additional input, thereby reducing prediction errors. However, generating a simulation mesh for new geometries is computationally costly. In contrast, mesh-free methods, which do not rely on the simulation mesh, typically incur higher errors. Motivated by these considerations, we introduce SMART, a neural s
The continuous advancements in transformer models and increasing demand for efficient simulation methods are driving innovation in AI for scientific computing.
This development allows for faster and more accessible aerodynamic simulations, potentially accelerating design cycles in industries like automotive and aerospace.
Traditional computationally intensive, mesh-dependent simulations can be replaced or augmented by more efficient mesh-free AI surrogates, democratizing access to high-fidelity design tools.
- · Aerospace Industry
- · Automotive Industry
- · AI/ML companies specializing in scientific computing
- · Design Engineers
- · Traditional CFD software vendors slow to adapt
- · Companies heavily invested in mesh generation technology
Reduced lead times and costs for product development involving complex fluid dynamics.
Increased innovation and iterative design capabilities across various engineering sectors due to lower simulation barriers.
Potential for broader adoption of AI-driven design optimization tools, leading to more performant and energy-efficient products.
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Read at arXiv cs.AI